# _*_coding:utf-8_*_


import numpy as np
from numpy.random import seed


class AdalineSGD(object):
    """ADAptive Linear Neuron classifier
    Parameters
    -----------------------------------
    eta:float
        Learning rate (between 0.0 and 1.0)
    n_iter:int
        passes over the training dataset

    Attributes
    ----------------------------------
    w_:id-array
        Weights after fitting
    errors_:list
        Number of misclassifications in every epoch.
    shuffle:bool(default:True)
        Shuffles training data every epoch
        if True to prevent cycles
    random_state:int(default:None)
        set random state for shuffling
        and initializing the weight.

    """

    def __init__(self, eta=0.01, n_iter=50, shuffle=True, random_state=None):
        self.eta = eta
        self.n_iter = n_iter
        self.w_initialized = False
        self.shuffle = shuffle
        if random_state:
            seed(random_state)

    def fit(self, X, y):
        """Fit training data
        Parameters
        ---------------------------------
        X:{Array-like},shape = [n_asmples.n_features]
            Training vectors,
            where n_samples is the number of samples and
            n_features is the number of features
        y:array-like,shape = [n_samples]
        Target values.

        Returns
        ----------------------------------
        self:object

        """
        self._initialize_weights(X.shape[1])
        self.cost_ = []
        for i in range(self.n_iter):
            if self.shuffle:
                X, y = self._shuffle(X, y)
            cost = []
            for xi,target in zip(X,y):
                cost.append(self._update_weights(xi,target))
            avg_cost = sum(cost)/len(y)
            self.cost_.append(avg_cost)
        return self

    def partial_fit(self, X, y):
        """Fit training data without reinitializing the weights"""
        if not self.w_initialized:
            self._initialize_weights(X.shape[1])
        if y.ravel().shape[0] > 1:
            for xi, target in zip(X, y):
                self._update_weights(xi, target)
        else:
            self._update_weights(X, y)
        return self

    def _shuffle(self, X, y):
        """Shuffle training data"""
        r = np.random.permutation(len(y))
        return X[r], y[r]

    def _initialize_weights(self, m):
        """Initialize weights to zeros"""
        self.w_ = np.zeros(1 + m)
        self.w_initialized = True

    def _update_weights(self, xi, target):
        """Apply Adalines learning rule to update the weights"""
        output = self.net_input(xi)
        errors = (target - output)
        self.w_[1:] += self.eta * xi.T.dot(errors)
        self.w_[0] += self.eta * errors.sum()
        cost = (errors ** 2).sum() / 2.0
        return cost

    def net_input(self, X):
        """calculate net input"""
        return np.dot(X, self.w_[1:]) + self.w_[0]

    def activation(self, X):
        """compute linear activation"""
        return self.net_input(X)

    def predict(self, X):
        """return class label after unit step"""
        return np.where(self.activation(X) >= 0.0, 1, -1)
